Shaowu Pan, Ph.D., assistant professor of mechanical, aerospace, and nuclear engineering at Rensselaer Polytechnic Institute (RPI), has recently received two prestigious awards recognizing his work to solve some of engineering’s most difficult challenges.  

This summer, Pan was awarded a Google Research Scholar Award to support the development of an artificial intelligence (AI) assistant that will make Computational Fluid Dynamics (CFD) faster, more accessible, and more productive for engineers worldwide. He also recently received funding from General Electric Vernova (GEV) through an award from the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) to advance hydrogen sensing and diagnostics to ensure the safe and economical operation of hydrogen energy infrastructure. Both honors highlight Pan’s commitment to tackling complex engineering problems with innovative solutions.

Building and configuring CFD simulations is notoriously time-consuming and complex, but Pan’s AI assistant is designed to streamline these tasks, significantly improving productivity while lowering the steep learning curve for newcomers. To measure progress, his team has also developed the first comprehensive benchmark for evaluating large language models (LLMs) on CFD tasks.

Pan was inspired to pursue this work after repeatedly facing the challenge of applying complex AI methods to equally complex engineering problems. “Much of my research is about finding ways to make very difficult tasks more manageable,” he said. “The AI assistant is one example of how we can design smarter tools to help engineers focus on discovery and innovation.”

His funding from GEV through the ARPA-E award expands that vision into the energy sector. Sensor analytics of atmospheric emissions of hydrogen is very challenging due to the combined effect of micro-weather, environmental flows around built infrastructure, and limited sensing options. But Pan, in collaboration with GEV Advanced Research center, is developing cutting edge sensor analytics solutions: “By developing methods to detect and quantify hydrogen leaks, this research will help mitigate safety risks and strengthen hydrogen’s role as a clean energy source,” Pan said. The project also has potential applications in weather forecasting, such as predicting how strong winds interact with buildings.

Together, these awards highlight the impact of Pan’s research at RPI, which spans computational fluid dynamics, data-driven modeling of complex systems, scientific machine learning, and generative AI. By combining theory, computation, and AI, his work is transforming how engineers approach some of the world’s most pressing challenges.